A Particle Swarm Optimization based a two-storage model for deteriorating items with Transportation Cost and Advertising Cost: The Auto Industry
Abstract & Details
Research Area
Applied Mathematics
Keywords
Two-warehouses
deterministic inventory
Transportation Cost
Advertising Cost and Particle Swarm Optimization
Abstract
A deterministic inventory model of Auto industry article deterioration using Particle Swarm Optimization (PSO) has been developed. He advocates a type of ramp with inflation effects in dual storage facilities of Auto industry. The own warehouse of Auto industry has a fixed capacity of W. units. The rented warehouse (RW) of Auto industry has unlimited capacity. We have assumed here that storage costs of Auto industry in RW were higher than in OW. Inventory of Auto industry bottlenecks are allowed and partially deferred using Particle Swarm Optimization (PSO) assumes that inventory of Auto industry will deteriorate over time with a variable rate of deterioration of Auto industry. The inflation effect has also been taken into account for various costs associated with the inventory system of Auto industry and the using Particle Swarm Optimization (PSO). The numerical example is also used to examine the behaviour of the model. The cost minimization technique is used to obtain the terms of total cost and other parameters.
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Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Ajay Singh Yadav | SRM Institute of Science and Technology (formerly known as SRM University), Delhi-NCR Campus, Ghaziabad, U.P., India. |
| 2 | Navin Ahlawat | SRM Institute of Science and Technology (formerly known as SRM University), Delhi-NCR Campus, Ghaziabad, U.P., India |
| 3 | Anupam Swami | Govt. P.G. College Sambhal, U.P., India |
| 4 | Geethanjali Kher | Kirori Mal College, Delhi, India |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Yadav, Ajay Singh, Ahlawat, Navin, Swami, Anupam, & Kher, Geethanjali (2019). A Particle Swarm Optimization based a two-storage model for deteriorating items with Transportation Cost and Advertising Cost: The Auto Industry. International Journal of Advance Research and Innovative Ideas In Education, 5(1), 34-44.
MLA Style
Yadav, Ajay Singh, et al. "A Particle Swarm Optimization based a two-storage model for deteriorating items with Transportation Cost and Advertising Cost: The Auto Industry." International Journal of Advance Research and Innovative Ideas In Education, vol. 5, no. 1, 2019, pp. 34-44.
IEEE Style
Ajay Singh Yadav, Navin Ahlawat, Anupam Swami, and Geethanjali Kher, "A Particle Swarm Optimization based a two-storage model for deteriorating items with Transportation Cost and Advertising Cost: The Auto Industry," International Journal of Advance Research and Innovative Ideas In Education, vol. 5, no. 1, pp. 34-44, 2019.
Vancouver Style
Yadav Ajay Singh, Ahlawat Navin, Swami Anupam, Kher Geethanjali. A Particle Swarm Optimization based a two-storage model for deteriorating items with Transportation Cost and Advertising Cost: The Auto Industry. International Journal of Advance Research and Innovative Ideas In Education. 2019;5(1):34-44.
Harvard Style
Yadav, Ajay Singh, Ahlawat, Navin, Swami, Anupam, & Kher, Geethanjali (2019) 'A Particle Swarm Optimization based a two-storage model for deteriorating items with Transportation Cost and Advertising Cost: The Auto Industry', International Journal of Advance Research and Innovative Ideas In Education, 5(1), pp. 34-44.
Chicago Style
Yadav, Ajay Singh, et al. "A Particle Swarm Optimization based a two-storage model for deteriorating items with Transportation Cost and Advertising Cost: The Auto Industry." International Journal of Advance Research and Innovative Ideas In Education 5, no. 1 (2019): 34-44.
Turabian Style
Yadav, Ajay Singh, et al. "A Particle Swarm Optimization based a two-storage model for deteriorating items with Transportation Cost and Advertising Cost: The Auto Industry." International Journal of Advance Research and Innovative Ideas In Education 5, no. 1 (2019): 34-44.
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